Zobrazeno 1 - 10
of 2 288
pro vyhledávání: '"Ngadiuba Jennifer"'
Autor:
Wang, Aaron, Gandrakota, Abhijith, Ngadiuba, Jennifer, Sahu, Vivekanand, Bhatnagar, Priyansh, Khoda, Elham E, Duarte, Javier
Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC. Particle Transformer (ParT), a stat
Externí odkaz:
http://arxiv.org/abs/2412.03673
Autor:
Baldi, Tommaso, Campos, Javier, Hawks, Ben, Ngadiuba, Jennifer, Tran, Nhan, Diaz, Daniel, Duarte, Javier, Kastner, Ryan, Meza, Andres, Quinnan, Melissa, Weng, Olivia, Geniesse, Caleb, Gholami, Amir, Mahoney, Michael W., Loncar, Vladimir, Harris, Philip, Agar, Joshua, Qin, Shuyu
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for
Externí odkaz:
http://arxiv.org/abs/2406.19522
Model size and inference speed at deployment time, are major challenges in many deep learning applications. A promising strategy to overcome these challenges is quantization. However, a straightforward uniform quantization to very low precision can r
Externí odkaz:
http://arxiv.org/abs/2405.00645
Autor:
Odagiu, Patrick, Que, Zhiqiang, Duarte, Javier, Haller, Johannes, Kasieczka, Gregor, Lobanov, Artur, Loncar, Vladimir, Luk, Wayne, Ngadiuba, Jennifer, Pierini, Maurizio, Rincke, Philipp, Seksaria, Arpita, Summers, Sioni, Sznajder, Andre, Tapper, Alexander, Aarrestad, Thea K.
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the inpu
Externí odkaz:
http://arxiv.org/abs/2402.01876
The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models with low comp
Externí odkaz:
http://arxiv.org/abs/2311.14160
Autor:
Pol Adrian Alan, Aarrestad Thea, Govorkova Katya, Halily Roi, Kopetz Tal, Klempner Anat, Loncar Vladimir, Ngadiuba Jennifer, Pierini Maurizio, Sirkin Olya, Summers Sioni
Publikováno v:
EPJ Web of Conferences, Vol 251, p 04027 (2021)
We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of
Externí odkaz:
https://doaj.org/article/cdaa8b5f1b46459e8e7aac20a08d5b03
Autor:
Woźniak Kinga Anna, Cerri Olmo, Duarte Javier M., Möller Torsten, Ngadiuba Jennifer, Nguyen Thong Q., Pierini Maurizio, Spiropulu Maria, Vlimant Jean-Roch
Publikováno v:
EPJ Web of Conferences, Vol 245, p 06039 (2020)
We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on t
Externí odkaz:
https://doaj.org/article/f070f05f774541e8917af87521ee9c1a
Autor:
Ghielmetti, Nicolò, Loncar, Vladimir, Pierini, Maurizio, Roed, Marcel, Summers, Sioni, Aarrestad, Thea, Petersson, Christoffer, Linander, Hampus, Ngadiuba, Jennifer, Lin, Kelvin, Harris, Philip
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network ar
Externí odkaz:
http://arxiv.org/abs/2205.07690
Autor:
Harris, Philip, Katsavounidis, Erik, McCormack, William Patrick, Rankin, Dylan, Feng, Yongbin, Gandrakota, Abhijith, Herwig, Christian, Holzman, Burt, Pedro, Kevin, Tran, Nhan, Yang, Tingjun, Ngadiuba, Jennifer, Coughlin, Michael, Hauck, Scott, Hsu, Shih-Chieh, Khoda, Elham E, Chen, Deming, Neubauer, Mark, Duarte, Javier, Karagiorgi, Georgia, Liu, Mia
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML
Externí odkaz:
http://arxiv.org/abs/2203.16255
Autor:
Pol, Adrian Alan, Aarrestad, Thea, Govorkova, Ekaterina, Halily, Roi, Klempner, Anat, Kopetz, Tal, Loncar, Vladimir, Ngadiuba, Jennifer, Pierini, Maurizio, Sirkin, Olya, Summers, Sioni
We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN Large Hadron Collider (LHC). Collision events produced at the LHC and represented as an image co
Externí odkaz:
http://arxiv.org/abs/2202.04499